Burnhambengtsen7808

Torque density is one of the major limiting factors in machine design. In this paper, we propose the hybrid excited partitioned stator switched flux machine3 (HE-PSSFM3). In HE-PSSFM3, armature winding is positioned on the outer stator whereas the permanent magnet (PM) and field winding are placed at the inner stator, while the rotor is free from excitation sources and armature winding. Moreover, concentrated field winding is replaced by toroidal winding. DNQX clinical trial The power splitting ratio between two stators/rotor pole combinations is analytically optimized and are validated through genetic algorithm (GA) in order to enhance average torque and flux regulation capability. The electromagnetic characteristics of the improved and initial design are evaluated and compared with existing designs, i.e., HE-PSSFM1 and HE-PSSFM2. The proposed HE-PSSFM3 has achieved high average torque, i.e., 2.0015 Nm, at same armature and field current densities of 5 A/mm2. The results show that the average torques of the proposed design are 35% and 15% greater than HE-PSSFM1 and HE-PSSFM2, respectively. Furthermore, the analysis of various parameters such as flux linkage, flux regulation, electromagnetic performances, cogging torque, back EMF, electromagnetic torque, and torque ripples are investigated using two dimensional (2D) finite element analysis (FEA). Moreover, the simulation results of the proposed design are validated through GA and analytical modeling.In this paper, the most efficient (from data compaction point of view) and current image lossless coding method is presented. Being computationally complex, the algorithm is still more time efficient than its main competitors. The presented cascaded method is based on the Weighted Least Square (WLS) technique, with many improvements introduced, e.g., its main stage is followed by a two-step NLMS predictor ended with Context-Dependent Constant Component Removing. The prediction error is coded by a highly efficient binary context arithmetic coder. The performance of the new algorithm is compared to that of other coders for a set of widely used benchmark images.The paper addresses the dynamics of education by using Markov chains, a powerful probabilistic model able to make predictions on how sources of knowledge either change or stabilize over adulthood. To this end, each student filled in a survey that rated, on a scale from 1 to 5, the utility of five different sources of knowledge. They completed this survey twice, once for their previous and once for their current education. The authors then fitted a Markov chain to these data-essentially, calculating transition probabilities from one ranking of sources of knowledge to another-and inferred the final maximum utility sources of knowledge via the stationary distribution. The overall conclusion is the following even if the professor used to play a crucial role in early development, students have the tendency to become independent in their learning process, relying more on online materials and less on printed books and libraries.Entropy is a powerful tool for quantification of the brain function and its information processing capacity. This is evident in its broad domain of applications that range from functional interactivity between the brain regions to quantification of the state of consciousness. A number of previous reviews summarized the use of entropic measures in neuroscience. However, these studies either focused on the overall use of nonlinear analytical methodologies for quantification of the brain activity or their contents pertained to a particular area of neuroscientific research. The present study aims at complementing these previous reviews in two ways. First, by covering the literature that specifically makes use of entropy for studying the brain function. Second, by highlighting the three fields of research in which the use of entropy has yielded highly promising results the (altered) state of consciousness, the ageing brain, and the quantification of the brain networks' information processing. In so doing, the presy, it identifies that the complexity, as quantified by entropy, is a fundamental property of conscious experience, which also plays a vital role in the brain's capacity for adaptation and therefore whose loss by ageing constitutes a basis for diseases and disorders. Interestingly, these two perspectives neatly come together through the association of entropy and the brain capacity for information processing.A permanent challenge in physics and other disciplines is to solve Euler-Lagrange equations. Thereby, a beneficial investigation is to continue searching for new procedures to perform this task. A novel Monte Carlo Metropolis framework is presented for solving the equations of motion in Lagrangian systems. The implementation lies in sampling the path space with a probability functional obtained by using the maximum caliber principle. Free particle and harmonic oscillator problems are numerically implemented by sampling the path space for a given action by using Monte Carlo simulations. The average path converges to the solution of the equation of motion from classical mechanics, analogously as a canonical system is sampled for a given energy by computing the average state, finding the least energy state. Thus, this procedure can be general enough to solve other differential equations in physics and a useful tool to calculate the time-dependent properties of dynamical systems in order to understand the non-equilibrium behavior of statistical mechanical systems.Obstructive sleep apnea (OSA) is a fatal respiratory disease occurring in sleep. OSA can induce declined heart rate variability (HRV) and was reported to have autonomic nerve system (ANS) dysfunction. Variance delay fuzzy approximate entropy (VD_fApEn) was proposed as a nonlinear index to study the fluctuation change of ANS in OSA patients. Sixty electrocardiogram (ECG) recordings of the PhysioNet database (20 normal, 14 mild-moderate OSA, and 26 severe OSA) were intercepted for 6 h and divided into 5-min segments. HRV analysis were adopted in traditional frequency domain, and nonlinear HRV indices were also calculated. Among these indices, VD_fApEn could significantly differentiate among the three groups (p less then 0.05) compared with the ratio of low frequency power and high frequency power (LF/HF ratio) and fuzzy approximate entropy (fApEn). Moreover, the VD_fApEn (90%) reached a higher OSA screening accuracy compared with LF/HF ratio (80%) and fApEn (78.3%). Therefore, VD_fApEn provides a potential clinical method for ANS fluctuation analysis in OSA patients and OSA severity analysis.